Deep Q-Learning (DQN)
Deep Q-Learning (DQN) is a reinforcement learning algorithm that uses a deep neural network to approximate the Q-function. The Q-function is a function that maps from a state-action pair to the expected cumulative reward of taking that action in that state. DQN works by iteratively updating the neural network's parameters based on the agent's experience. The agent interacts with the environment and receives rewards for taking actions that lead to desired outcomes. The neural network is then updated to reflect the agent's new knowledge about the environment. DQN is a powerful algorithm that has been used to achieve state-of-the-art results in a variety of games, including Atari, Go, and StarCraft. It is a versatile algorithm that can be used to solve a wide range of decision-making problems. Here are some of the key concepts in Deep Q-Learning: Q-function: The Q-function is a function that maps from a state-action pair to the expected cumulative reward of taking that acti...